MAELSTROM Objective 2: ML Removal from seabed and both lower and upper water column
The robotic seabed cleaning platform developed by TECNALIA, CNRS- LIRMM and “Servizi Tecnici”, consists in a floating platform which, through cables and winches, the seabed cleaning robot is attached. The structure is equipped with a set of sensors for underwater perception to control the robot and detect & identify the marine litter to be removed. Moreover, the robotic platform is characterized by two different tools that allow to collect the ML on the seabed: a drudge to suck up smaller litter and a gripper to grasps larger items like tires, parts of boats, fishing nets etc.
Robotic seabed cleaning platform - video
The multiplication of publicly available datasets makes it possible to develop Deep Learning models for many real-world applica tions. However, some domains are still poorly explored, and their related datasets are often small or inconsistent. In addition, some biases linked to the dataset construction or labeling may give the impression that a model is particularly efficient. Therefore, evaluating a model requires a clear understanding of the database. Moreover, a model often reflects a given dataset’s performance and may deteriorate if a shift exists between the training dataset and real-world data. In this paper, we derive a more consistent and balanced version of the TrashCan [6] image dataset, called UNO, to evaluate models for de tecting non-natural objects in the underwater environment. We pro pose a method to balance the number of annotations and images for cross-evaluation. We then compare the performance of a SOTA object detection model when using TrashCAN and UNO datasets. Addition ally, we assess covariate shift by testing the model on an image dataset for real-world application. Experimental results show significantly better and more consistent performance using the UNO dataset. The UNO database and the code are publicly av https://github.com/CBarrelet/balanced_kfold
From TrashCan to UNO: Deriving an Underwater Image Dataset To Get a More Consistent and Balanced Version
As a contribution to the development of new techniques to remove marine litter from the seabed of sees and oceans, the Robotic Seabed Cleaning Platform has been designed, built and experimented in the framework of the European Union project MAELSTROM. It es sentially consists of a oating platform that supports the base elements of a 6 degree-of-freedom cable-driven parallel robot actuated by eight winches. The mobile platform of this robot can work underwater and is equipped with sensors to control its underwater motions and to detect & identify marine litter. To achieve e cient and selective litter removal, an aspiration system and a gripper are installed on the CDPR underwater mobile platform.
The Robotic Seabed Cleaning Platform: An Underwater Cable-Driven Parallel Robot for Marine Litter Removal
This objective supports the implementation of the UN SDG 14 Life Below Water, by addressing two major challenges: (i)Surface and Upper Water Column litter: Floating plastics carried by rivers and harbours currents must be intercepted and captured before they enter the oceans and either sink to the seabed or create gyres MAELSTROM will deploy an automated air-bubbles barrier installed in optimized locations to retrieve these floating items, and also contribute to the re-oxygenation of the rivers, harbours and lagoons. (ii)Seabed and Lower Water Column: For ML hotspots in coastal waters, MAELSTROM will provide a robotized cable robot capable of high efficiency removal of small and large debris on seabed and in the lower water column.
MAELSTROM Objective 2: ML Removal from seabed and both lower and upper water column - archive
© MAELSTROM - Smart technology for MArinE Litter SusTainable RemOval and Management funded by the European Union, Programme H2020-EU.3.2.5.1. Grant agreement No 101000832. https://doi.org/10.3030/101000832.
Fantina Madricardo, Vanessa Moschino, ANTONIO PETRIZZO, Taha Lahami, and Valentina Grande. "MAELSTROM Objective 2: ML Removal from seabed and both lower and upper water column - archive." ROHub. May 28 ,2025. https://doi.org/10.24424/ddnx-hs06.
Petrizzo, A., MOSCHINO, V., Madricardo, F., Ghezzo, M., Galvez, D., Rodriguez, M., & ferrari, . nicola . (2022). D5.1 Report on site identification and installation of the Seabed Cleaning System in Venice. MAELSTROM Project. https://doi.org/10.5281/zenodo.14929590
D5.1 Report on site identification and installation of the Seabed Cleaning System in Venice
Gouttefarde, M., & Barrelet, C. (2024). D3.3 Preliminary report on the Cable robot autonomous control using Machine learning for litter identification. MAELSTROM Project. https://doi.org/10.5281/zenodo.14929678
D3.3 Preliminary report on the Cable robot autonomous control using Machine learning for litter identification
Ehrhorn, P., Iglesias, I., Vieira, L., & Sousa Pinto, I. (2021). D5.3 Definition of location for surface/water column removal device in the Porto region, Portugal. MAELSTROM Project. https://doi.org/10.5281/zenodo.14930072
D5.3 Definition of location for surface/water column removal device in the Porto region, Portugal
Rodriguez, M., & Gouttefarde, M. (2022). D3.1 Report on the cable robot design and teleoperated control. MAELSTROM Project. https://doi.org/10.5281/zenodo.14931001
D3.1 Report on the cable robot design and teleoperated control
Gouttefarde, M., & Barrelet, C. (2024). D3.2 Report and videos about the cable robot control with shared autonomy. MAELSTROM Project. https://doi.org/10.5281/zenodo.14931140
D3.2 Report and videos about the cable robot control with shared autonomy
Iglesias, I., Vieira, L., Antunes, S., Kett, G., Fantinati, D. del O. A., Nogueira, S., Bio, A., Sousa-Pinto, I., Buschman, F. A., Mira Veiga, J., Pessoa, A., Zingariello, D., & Mule'Stagno, L. (2025). D5.4 Final Report on operation of the surface and water column removal technology in the Porto region. MAELSTROM Project. https://doi.org/10.5281/zenodo.15544106
D5.4 Final Report on operation of the surface and water column removal technology in the Porto region
Ferrari, N., Fantin, A., Rodríguez Mijangos, M., Sallé, D., Herve, P.-E., Gorrotxategi, J., Oyarzabal, A., Culla, D., Gouttefarde, M., Creuze, V., Barrelet, C., Temperini, H. O., Petrizzo, A., MOSCHINO, V., Mesghez, S., Lahami, T., Lorenzetti, G., & Madricardo, F. (2025). D5.2 Final report on operation of the Seabed Cleaning System in Venice. MAELSTROM Project. https://doi.org/10.5281/zenodo.15546289
D5.2 Final report on operation of the Seabed Cleaning System in Venice
Environment/Natural resources/Water/Rivers
MAELSTROM Objective 2: ML Removal from seabed and both lower and upper water column - archive.
Economy, business and finance/Economic sector/Chemicals/Synthetic and plastic chemicals
This objective supports the implementation of the UN SDG 14 Life Below Water, by addressing two major challenges: (i)Surface and Upper Water Column litter: Floating plastics carried by rivers and harbours currents must be intercepted and captured before they enter the oceans and either sink to the seabed or create gyres MAELSTROM will deploy an automated air-bubbles barrier installed in optimized locations to retrieve these floating items, and also contribute to the re-oxygenation of the rivers, harbours and lagoons.
Environment/Natural resources/Water/Oceans
(ii)Seabed and Lower Water Column: For ML hotspots in coastal waters, MAELSTROM will provide a robotized cable robot capable of high efficiency removal of small and large debris on seabed and in the lower water column.
Robotic Seabed Cleaning Platform explained by Damien Salle
Piattaforma Robotica per la Pulizia dei Fondali Marini
Robotic Seabed Cleaning Platform – Removing Marine Litter from Venice Lagoon (Maelstrom Project)
Three underwater macro-litter image datasets for object detection in YOLO format: UNO dataset: All details are available in From TrashCan to UNO: Deriving an Underwater Image Dataset to Get a More Consistent and Balanced Version, C.Barrelet et al, ICPR 2022 MORGANE dataset: Images taken in shallow water in the harbors near Montpellier, France VENICE dataset: Images taken during the MAELSTROM experiments in Venice, Italy
Bubble Barrier Vila do Conde - MAELSTROM Partners
Bubble Barrier Vila do Conde catches plastic pollution before it flows into the Atlantic Ocean